Delivering Improved Retail Service At A Lower Cost
The Best People Doing The Right Things At The Right Time
What was the challenge?
A high street retailer needed to reduce costs and wanted to shrink their in-store labour hours whilst at the same time improving customer service. We were asked to apply our retail and analytical expertise to review the existing labour scheduling practices and design methods for achieving this goal.
What was the approach?
We adopted a two-pronged approach to the analysis of this problem. The first was to work in-store alongside the store colleagues to identify the day to day duties, how they are carried out and which processes absorbed the most time. The second approach was to assess footfall, transaction and scheduling data to see how well staffing levels were matched to customer needs. These approaches quickly identified a number of low value activities that could be improved or eliminated in order to reduce labour requirements. At the same time, it was clear that the full time/ part time mix of the store colleagues meant that labour scheduling was not well matched to customer footfall. The overall impact of this was poor customer service at peak times and unused labour capacity during quieter periods.
We then worked with the business, HR and IT partners to introduce a labour scheduling tool that could predict the peak times for customer engagement and thereby allocate staff appropriately. We improved the flexibility of working hours by implementing a revised recruitment model, this enabled us to migrate a significant proportion of the store hours to a part time basis. In addition to this we also improved a number of store processes in order to release additional capacity for customer engagement.
How did we help?
Through this project we showed how a more structured approach to labour management could provide significant improvements in customer satisfaction at a lower net cost and we helped provide the tools to achieve this. As an outcome, the client reduced customer transaction times by 50% and improved store EBITDA by 16%